K. ZEBBICHE , F. KHELIFI and A. BOURIDANE Multibit Decoding of Multiplicative Watermarking for Fingerprint Images K. ZEBBICHE , F. KHELIFI and A. BOURIDANE School of Electronics, Electrical Engineering and Computer Science Queen’s University Belfast In this talk, I am going to address the problem of u. I will .

Outline Introduction Proposed work Experimental results Conclusions In this presentation, I will first give a brief introduction to fingerprint-based systems and give some risks that may threat a fingerprint-based system, I will also introduce, in general, the watermarking techniques. After that, I will explain the different steps of the proposed work. Followed by experimental results and Finally I conclude my talk with a summary.

Introduction (Fingerprint-based Systems) Fingerprint-based authentication is the most mature, proven and widely used technique all over the world. Fingerprint-based system weaknesses: Not replaceable; Not secret; Several possible attacks may affect a biometric system. The fingerprint-based authentication is the most mature, proven and widely used technique all over the world. Although fingerprint data provide uniqueness, they do not provide secrecy because fingerprint data are: not replaceable :you can’t replace your fingerprint not secret: for example you can leave your fingerprint on any surface you touch, anyone can record your voice or get a photo of you. And most of all is that biometric systems may risk several threats. Ratha et al. described eight possible attacks in biometric system.

Introduction (Fingerprint-based attacks) Sensor Feature Extraction Matcher Stored Templates 1 3 2 4 5 8 6 7 1. Fake Biometrics 2. Replay Attack 3. Override 4. Tamper with features 5. Corruption match score 6. Tamper with templates (DB) 7. Intercept and modify 8. Override the final decision Ratha et al. described eight possible attacks in biometric system. To increase the security of the fingerprint data, some techniques have been introduced, among them, we find cryptography and watermarking (which is the technique used in our work). Solutions : Cryptography; Watermarking

Introduction (Watermarking Overview) Watermarking by definition is the fact to add an information to the host data. The embedded information may be recovered later on and used to check the authenticity of this host data. Embedding Decoding Detecting Watermark Extraction Detection Secret key K Watermark Insertion b x Pz/y z y Channel Exist Not Exist General model of a watermarking system. Watermarking by definition is the fact to add an information (ID, or logo) called watermark to the host data to be watermarked such that the watermark is unobtrusive, secure and also partly or fully be recovered from the host data later on. A general model of a watermarking system is given in this figure. In general, two stages can be described : The embedding stage : which aims to embed a watermark into an image while keeping the visual appearance of the image unaltered. this watermarked image is usually Transmitted trough a channel or stored in databases. In the second stage, which called also the receiver side, two processes can be defined: The watermark decoding or extraction : which aims to estimate the hidden the information bits. And the watermark detection : which aims to detect the presence or not of a given watermark.

Introduction (Watermarking Fingerprint Images) Protecting the originality of fingerprint images; Fraud detection in fingerprint images; Guaranteed secure transmission. Watermarking of fingerprint images can be used in applications like: Protecting the originality of fingerprint images stored in databases against intentional and unintentional attacks, b) Fraud detection in fingerprint images by means of fragile watermarks (which do not resist to any operations on the data and get lost, thus indicating possible tampering of the data), c) Guaranteeing secure transmission of acquired fingerprint images from intelligence agencies to a central image database.

Proposed work (Watermark embedding) The watermark is embedded into high resolution subbands of the DWT. Watermark is embedded in spread-spectrum fashion. The DWT subband is partitioned into blocks and every block carries one bit. Pseudo random sequence is also divided into chunks. Every chunk is multiplied by +1 (or -1) according to the information bit to get an amplitude-modulated watermark. A multiplicative rule is used to embed the watermark. in our work and for the embedding process, we propose to embed the watermark bits: * into high resolution subbands of the DWT for imperceptibility reasons. Watermark is embedded in spread-spectrum fashion, which means that one bit is carried by a set of coefficients. The DWT subband is partitioned into blocks and every block carries one bit. Pseudo random sequence which is also divided into chunks. every chunk is associated the one bit and multiplied by 1 or -1 according to its associated bit to get an amplitude-modulated watermark. * The multiplicative rule is used to embed the watermark.

Proposed work (Watermark embedding) bk Encoder Message Strength γ PRS Generator m Mk Secret key K Dividing Watermarked Image y x xk Original Image DWT Partitioning Reconstructing IDWT The embedding process is summarized by this block diagram. A discrete wavelet transform is applied to the original image to obtain the different subbands. Each subband is partitioned into blocks and every block will be used to carry one bit. Then, a pseudo random sequence is generated using a secret key and this sequence is divided into chunks. Every chunk is multiplied by one bit information to get an amplitude modulated watermark which is inserted into the blocks using the multiplicative rule. Note that the value to each coefficient is controlled by a factor called the strength of the watermark. After that, the watermarked blocks are gathered to reconstitute the marked subbands and an inverse discrete wavelet transform is applied to these marked suubands to obtain the watermarked image. Block diagram of the watermark embedding process

Proposed method (Watermark Decoding ) By assuming equally probable information bits. The optimum decoding is based on the maximum-likelihood estimation scheme. The DWT coefficients are modelled by the generalized Gaussian distribution (GGD). The GGD parameters are estimated directly from the watermarked image. The main concepts of the decodeing side are : - We assume that all information bits are equally probable which means that all the possible information bits could be hidden in the watermarked image. - The optimum decoding is based on the maximum- likelihood scheme which requires an accurate statistical modelling of the DWT coefficients, and the optimality of the proposed decoder depends on the accuracy of the modelling of these coefficients. - It has been found that the generalized Gaussian is the best distribution that can model the DWT coefficients. - The GGD parameters are estimated directly from the watermarked image.

Proposed method (Watermark Decoding ) α ,β Watermarked Image x xk Estimating α and β DWT Partitioning m Mk PRS Generator Decoded Message Secret key K Dividing Decoder The decoding process is summarized by this block diagram. A discrete wavelet transform is applied to the watermarked image to obtain the different subbands. Each subband is partitioned into blocks as in the embedding side. The GGD parameters of each block is then estimated. Then, a pseudo random sequence is generated using the same secret key used in the embedding and this sequence is divided into chunks. The decoder uses the estimated parameters And the chunks to estimate the embedded information bits. Strength γ Block diagram of the watermark decoding process

Experimental results Real fingerprint images of size 448×478 (DB_3, FVC_2000), DWT coefficients are obtained using Daubechies wavelet at the 3rd level, Each subband is partitioned in blocks of size 1616 (256 coefficients/block), The performance of the decoder is evaluated by the Bit Error Rate (BER), Compare the performance with the multibit decoder proposed by Song (2005). Simulation results were conducted on real fingerprint images of size 448×478 chosen from ‘Fingerprint Verification Competition’ (DB_3, FVC2000) database, chosen to take into account the different quality of fingerprint images. DWT coefficients are obtained using Daubechies wavelet at the 3rd level, Each subband is partitioned in blocks of size 1616 (256 coefficients/block), The performance of the decoder is evaluated by the Bit Error Rate (BER), The performance of the proposed decoder is compared with the multibit decoder proposed by Song published in (2005).

Experimental results Original image Watermarked image Image of difference The difference between the host and corresponding the watermarked image, magnified by a factor of 20. As it can be seen, the watermark is concentrated in the region of the ridges, which makes the watermark more secure because any attempt to remove the watermark will affect the ridges which constitute the region of interest. Original image Watermarked image Image of difference Test fingerprint images. Strength is set to 0.26. Image of the difference magnified by 20.

Experimental results (Performance comparison) The proposed decoder outperforms the one proposed by Song. Comparison between the proposed decoder BER and the Song’s decoder BER, computed for different value of the strength. As can be seen in this figure, our proposed decoder outperforms the one proposed by Song for all values of the watermark strength.

Experimental results (Error correcting code “BCH”) - The BCH code increases the successful decoding rate but decreases the number of the hidden bits. The results obtained show that the BER decreases when the strength increases. Visual degradations appear at γ=0.28 (PSNR < 40) and higher, below this value the BER is low and the proposed decoder yields very attractive results. The BCH code increases significantly the successful decoding rate. It is worth mentioning that the use of code correcting error limits the number of the information bits to be hidden. For our example the Nb=36 without BCH code and Nb=16 with the BCH code. Comparison between simulation results BER with and without using BCH code, computed for different value of the strength.

Experimental results (Wavelet Scalar Quantization “WSQ” Compression) The BER of the proposed decoder is lower than that of Song’s decoder. Wavelet-based watermarking is very robust to the wavelet-based compression including WSQ compression. For the sake of completeness, experiments have also been conducted to assess the performance of the proposed decoder in terms of robustness against attacks such as the Wavelet Scalar Quantization (WSQ) compression which is the compression standard adopted by the FBI and many other investigation agencies , mean filtering and additive white Gaussian noise (AWGN). For the influence of the WSQ compression, as can be seen from this plot. The Bit error rate of the proposed decoder Is lower than that of Song’s decoder. And in general, the wavelet based watermarking is very robust to wavelet based compression including WSQ.

Experimental results (White Gaussian Noise Addition) The BER of the proposed decoder is much lower than that of Song’s decoder. The Gaussian noise does not affect significantly the performance of the proposed decoder. In the presence of the white gaussian noise addition, the difference between the decoders is significant. And the gaussian noise does not affect significantly the performance of the proposed decoder.

Experimental results (Mean filtering) Mean filtering alters significantly the visual appearance of the images. The two methods provide quite good results. The mean filtering affects and alters directly the DWT coefficients of high subbands where the watermark is embedded. The first notice about the influence of the mean filtering is that the mean filtering alters significantly The visual appearance of the images. And for the performance of he decoders, the two methods provide quite good results. These results was expected since the mean filtering affects and alters directly the high subbands coefficients Of the DWT where the watermark is embedded.

Conclusions An optimum decoder for fingerprint image watermarking in the DWT domain has been developed. The DWT coefficients are modelled by GGD. The parameters of the GGD are directly estimated from the watermarked image. The proposed decoder provides very attractive results and the BER is within acceptable range of tolerance. Error correcting code enhances significantly the results. The proposed decoder can be used for any host data that are statistically modelled by GGD. In this work, An optimum decoder for fingerprint images watermarking in the DWT domain has been developed. The DWT coefficients are modelled by the generalized Gaussian distribution whose parameters are directly estimated from the marked image. The proposed decoder provides very attractive results and the BER is within acceptable range of tolerance which can be further reduced by using Error correcting code. Although the results shown are concerned with the fingerprint images, the proposed decoder can be applied to any other host data That can statistically be modelled by GGD.